\jection{Treatment Learning}
\begin{frame}
\frametitle{What is Treatment Learning?}
Key elements are
\begin{itemize}
\item lift, ordering heuristic
\item minimum best support, over-fitting avoidance
\item small treatment effect, solution form
\end{itemize}
\end{frame}

\begin{frame}
\frametitle{What is Treatment Learning?}
Treatments are conjunctions of range restrictions
or assignments to some of the independent variables.

Lifts are the ratio of the normalized weighted
sums between treated instances and entire
training set.

\small
\begin{align*}
lift &= \frac{treated~average}{baseline} \\
\vspace{.4in}
 &= \frac{\sum_{i=0}^{n}(weight_i * treated_i)}
         {\sum_{i=0}^{n}(weight_i * prop_i)}
\end{align*}
\end{frame}

\begin{frame}
\frametitle{Why Do We Prefer Treatment Learning?}
%\large Explanatory vs.\ Performance systems
%\vspace{.35in}

\normalsize
\begin{itemize}
\item Theories returned by machine learners are supposed
      to \emph{generalize} from training data
\item Explanatory, as opposed to performance, systems should offer insight 
      to human users about hidden relationships in the data
\item Treatment learners offer theories
      that reveal these relations in a {\bf comprehensible} form 
\end{itemize}


\end{frame}


\begin{frame}
\frametitle{Example Treatment}
Training on Boston housing data, tar3 finds: \\
\fbox{\parbox{2.8in}{{\it rm} $\geq$ 6.6 $\wedge$ {\it ptratio} $\leq$ 15.9 $\Rightarrow$
 
\qquad {\bf P}({\it high}) = 97\%, {\bf P}({\it medhigh}) = 3\%}} \\
\vspace{.045in}
where the baseline distribution was \\
29\% {\it high}, 29\% {\it medhigh}, 21\% {\it medlow}, 21\% {\it low} \\
and each instance has 13 independent attributes

\vspace{.25in}
This treatment is small, comprehensible, and valuable.
\pause

But what does a theory look like from
another well-regarded data miner \ldots
\end{frame}

\begin{frame}
\frametitle{Example Decision Tree}

\begin{columns}[T]

\begin{column}{2in}
\includegraphics[wdith=2.00in]{../Greenwald06/Figures/bostontree.pdf}
\end{column}

\begin{column}[t]{2in}
This decision tree, while accurate, does not lend itself to easy
interpretation by human experts.
If the point of an explanatory system is to reveal relationships,
what does this tree reveal?  \\
\pause
\vspace{.25in}
Anything?
\end{column}

\end{columns}
\end{frame}
